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Background Fruit drop and quality deterioration are major constraints limiting date palm ( Phoenix dactylifera L.) productivity, particularly under environmental constraints. While plant growth regulators (PGRs), particularly synthetic auxins like 2,4-Dichlorophenoxyacetic acid (2,4-D), have demonstrated potential in managing these physiological limitations, cultivar-specific responses and optimal application protocols remain insufficiently characterized. Results This study evaluated the impact of foliar-applied 2,4-D at three concentrations (25, 50, and 75 mg/L) on fruit retention and quality traits in two date palm cultivars under field conditions. Application of 2,4-D, particularly at 50 mg/L during the kimri stage, significantly improved key agronomic parameters, including bunch weight, fruit and pulp weight, fruit length and width, and moisture content. Fruit quality parameters such as total soluble solids (TSS), total sugars, reducing and non-reducing sugars, ascorbic acid, and tannins were also enhanced. Furthermore, antioxidative enzymes peroxidase and catalase, flavonoids, and phenolic content were significantly higher compared to the untreated control. Among the tested cultivars, differential responses were observed, with one cultivar consistently showing superior outcomes in terms of fruit quality. Conclusion The exogenous application of 2,4-D demonstrated a positive effect on reducing fruit drop and enhancing both physical and quality traits in date palm cultivars. These findings suggest that 2,4-D can serve as an effective tool in sustainable date palm production by improving yield and fruit quality under stress conditions.

Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated cardiac MRI from highly undersampled k-t space measurements. We propose implementing the feature map and kernel function in the kernel method using CNNs. The parameters of the CNNs are learned from specific-subject data directly. Comparisons with state-of-the-art kernel methods show improved performance of the proposed deep kernel method.

Apricot is a globally cultivated temperate fruit with high nutritional and economic value. Gilgit Baltistan (GB), Pakistan, is a major apricot-producing region with unique geographic surroundings and rich germplasm diversity. However, the indigenous apricot accessions in this area have not yet been scientifically explored. This study provides the first integrated evaluation of 19 local apricot accessions using multivariate statistical analysis, including descriptive statistics, principal component analysis, correlation analysis, and hierarchical cluster analysis, to comprehensively assess morphological, pomological, and biochemical diversity among indigenous apricot accessions for conservation and crop improvement, and to identify elite accessions with promising horticultural and nutritional traits. The results revealed substantial variations in morphological traits, skin cracking susceptibility (56.270%), followed by texture of flesh (55.110%), flesh color (52.830%), over color pattern (49.970%), and harvesting maturity (49.430%). In correlation analysis, significant positive correlations were observed among key morphological and quality traits, including suture size with overcolor (r = 0.67) and fruit color with flesh color (r = 0.65), while harvesting maturity showed a negative correlation with color pattern (r = - 0.61), and stone separation was negatively associated with flesh juiciness (r = - 0.60). In biochemical assessments, notable diversity was recorded in total soluble solids (11.393-24.267 °Brix), titratable acidity (0.438-0.698%), total phenolic contents (27.721-121.078 mg GAE/100 g), total flavonoid contents (80.738-154.075 mg QE/g), reducing sugar (5.432-13.452%), total sugar (7.775-19.578%), and non-reducing sugar (2.172-6.448%). Furthermore, total lycopene contents (TLC), total anthocyanin contents (TAC), antioxidant activity (AOA), and total tannin contents (TTC) showed varied amounts from 2.880-19.720 mg/kg, 77.941-122.412 mg L⁻¹, 50.340-94.100%, 1.660-20.609 mg TAE/100 g, respectively, highlighting the unique nutraceutical potential. Principal Component Analysis (PCA) exhibited 78.582% of the total variability, highlighting fruit size, sugar composition, antioxidant-related traits, and color attributes as major contributors to diversity. In the biochemical correlation analysis, the strongest positive correlation was observed among total sugars, reducing sugars, non-reducing sugars, lycopene content, and crude fiber. Based on agronomic and biochemical quantitative traits, hierarchical cluster analysis divided these accessions into three clusters, and 'Yakyar' and 'Hawalpa' were identified as the most diverse accessions in biochemical characteristics. This study is novel in providing the first comprehensive phenotypic and biochemical characterization of GB apricot germplasm, revealing highly diverse and nutritionally valuable accessions that can be targeted for breeding, conservation, and functional food development.

Fruit drop is a serious challenge in date palm production, but it can be mitigated through various approaches. One of the most effective approaches for mitigating fruit drop is the foliar application of plant growth regulators (PGRs). Gibberellic acid (GA3) is a promising growth regulator that plays a pivotal role in plant growth and development, particularly in enhancing fruit quality and mitigating fruit drop. Exogenous application of GA3 has been extensively explored to address pre-harvest fruit drop in horticultural crops. This investigation is targeted to assess the impact of GA3 to mitigate fruit drop and enhance the quality of fruits in ‘Hillavi’ and ‘Khudravi’ date palm cultivars. The study was carried out at the Postgraduate Agriculture Research Station (PARS), Faisalabad, Pakistan, using a randomized complete block design (RCBD). Three GA3 concentrations (75 ppm, 150 ppm, and 225 ppm) were applied at the Kimri stage of date fruit development. The results revealed that exogenous spraying of GA3 significantly reduced fruit drop while improving fruit development and growth. Notable improvements were observed in bunch weight, pulp weight, fruit breadth, fruit weight, fruit length, moisture percentage, ascorbic acid, total sugar content, reducing and non-reducing sugars, total soluble solids (TSS), and tannin content. Additionally, the exogenous application of GA3 improved antioxidant enzyme activities, including peroxidase and catalase, along with flavonoid and phenolic contents as compared to the control treatment (Ck). This research highlights the potential of phytohormones, particularly GA3, as an effective strategy to mitigate fruit drop while enhancing fruit quality in date palm, underscoring their importance in sustainable horticultural practices.

Iterative shrinkage algorithms like parallel coordinate descent and separable surrogate functional use wavelet thresholding with uniform and empirically selected threshold values to recover the under-sampled magnetic resonance (MR) images. In this paper, an adaptive thresholding parameter, for the recovery of static and dynamic MR images, is derived and used in wavelet domain shrinkage. A modified iterative shrinkage thresholding algorithm based on the derived parameter is also proposed. Simulation results show that adaptive wavelet thresholding yields significantly higher signal-to-noise ratio and correlation than the fixed thresholding value. The algorithm based on the adaptive threshold is experimentally tested for static and dynamic MR images with varying acceleration rates, and it has been shown that it outperforms the fixed thresholding value algorithm.

Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication.

Motion correction is a challenging problem in free breathing undersampled cardiac perfusion magnetic resonance images. It is due to aliasing artifacts in the reconstructed images and the rapid contrast changes in the perfusion images. In addition to the reconstruction limitations, many registration algorithms underperforms in the presence of the rapid intensity changes. In this paper, we propose a novel motion correction technique that reconstructs the motion-free images from the undersampled cardiac perfusion MR data. The technique utilizes the robust principal component analysis along with the periodic decomposition to separate the respiratory motion component that can be registered, from the unchanged contrast intensity variations. It was tested on synthetic data, simulated data, and the clinically acquired data. The performance of the method was qualitatively assessed and validated by comparing manually acquired time–intensity curves of the myocardial sectors to automatically generated curves before and after registration.

Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data. These methods rely on sparsity and energy compaction priors on the factors to regularize the recovery. The quality of the recovered images depend on the specific priors. Motivated by deep image prior, we introduce a novel bilinear model whose factors are represented using convolutional neural networks (CNNs). The CNN parameters are learned from the undersampled data off the same subject. To reduce the run time and to improve performance, we initialize the CNN parameters. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on free breathing and ungated cardiac cine data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to low-rank and SToRM reconstructions.

We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank matrix completion method to directly fill in the missing k-space data from variable density spiral acquisitions. We validate the proposed scheme using simulated data and in-vivo data. Our results show that the proposed scheme provides improved reconstructions compared to the classical methods such as low-rank and XD-GRASP. The comparison with breath-held cine data shows that the quantitative metrics agree, whereas the image quality is marginally lower.

Motion free reconstruction of compressively sampled cardiac perfusion MR images is a challenging problem. It is due to the aliasing artifacts and the rapid contrast changes in the reconstructed perfusion images. In addition to the reconstruction limitations, many registration algorithms under perform in the presence of the rapid intensity changes. In this paper, we propose a novel motion correction method that reconstructs the motion free image series from the undersampled cardiac perfusion MR data. The motion correction method uses the novel robust principal component analysis based reconstruction along with the periodic decomposition to separate the respiratory motion component that can be registered, from the contrast intensity variations. It is tested on simulated data and the clinically acquired data. The performance of the method is qualitatively assessed and compared with the existing motion correction methods. The proposed method is validated by comparing manually acquired time-intensity curves of the myocardial sectors to automatically generated curves before and after registration.